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Counterfactual-Based Prevented and Preventable Proportions

Author

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  • Yamada Kentaro

    (Department of Statistical Science, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan)

  • Kuroki Manabu

    (Graduate School of Engineering, Yokohama National University, 79-1 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan)

Abstract

Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions because they are obtained according to different target populations and may fall outside the range [0.000,1.000]$[0.000,1.000]$. To solve this problem, this paper proposes counterfactual-based prevented and preventable proportions. When both causal effects and observed probabilities are available, we show that the proposed measures are identifiable under the negative monotonicity assumption. Additionally, when the negative monotonicity assumption is violated, we formulate the bounds on the proposed measures. We also show that negative monotonicity together with exogeneity induces equivalence between the proposed measures and existing measures.

Suggested Citation

  • Yamada Kentaro & Kuroki Manabu, 2017. "Counterfactual-Based Prevented and Preventable Proportions," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-15, September.
    • Handle: RePEc:bpj:causin:v:5:y:2017:i:2:p:15:n:6
    DOI: 10.1515/jci-2016-0020
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    References listed on IDEAS

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    1. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
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